h2o_shap: SHAP values for H2O Models

In lares: Analytics & Machine Learning Sidekick

SHAP values for H2O Models

Description

SHAP (SHapley Additive exPlanations) by Lundberg and Lee (2016) is a method to explain individual predictions. SHAP is based on the game theoretically optimal Shapley Values. Calculate SHAP values for h2o models in which each row is an observation and each column a feature. Use plot method to visualize features importance and distributions.

Usage

h2o_shap(model, test = "auto", scores = "auto", y = "y", ...)

## S3 method for class 'h2o_shap'
plot(x, relevant = TRUE, top = 15, quiet = FALSE, ...)

Arguments

model	h2o_automl object or h2o model.
test	String or Dataframe. Leave "auto" to use h2o_automl's test dataset or pass a valid dataframe.
scores	Numeric vector. If test != "auto", you must provide predicted values
y	Character. If test != "auto", you must provide y variable's name
...	Additional argument for predict_contributions.H2OModel
x	h2o_shap object
relevant	Boolean. Keep only relevant non-trivial (>0) features
top	Integer. Plot only top n values (as in importance)
quiet	Boolean. Print messages?

Value

H2OFrame with shap values for every observation and feature.

See Also

Other SHAP: shap_var()

Examples

## Not run: 
# Train a h2o_automl model
model <- h2o_automl(dft, Survived,
  max_models = 1, target = TRUE,
  ignore = c("Ticket", "Cabin", "PassengerId"),
  quiet = TRUE
)

# Calculate SHAP values
SHAP_values <- h2o_shap(model)
# Equivalent to:
# SHAP_values <- h2o_shap(
#  model = model$model,
#  test = model$datasets$test,
#  scores = model$scores_test$scores)

# Check SHAP results
head(SHAP_values)

# You must have "ggbeeswarm" library to use this auxiliary function:
# Plot SHAP values (feature importance)
plot(SHAP_values)

# Plot some of the variables (categorical)
shap_var(SHAP_values, Pclass)

# Plot some of the variables (numerical)
shap_var(SHAP_values, Fare)

## End(Not run)

lares documentation built on Sept. 13, 2024, 1:08 a.m.